Thomas L. Fillmore

Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum

Sensitive detection of low-abundance proteins in complex biological samples has typically been achieved by immunoassays that use antibodies specific to target proteins; however, de novo development of antibodies is associated with high costs, long development lead times, and high failure rates. To address these challenges, we developed an antibody-free strategy that involves PRISM (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing) for sensitive selected reaction monitoring (SRM)–based targeted protein quantification. The strategy capitalizes on high-resolution reversed-phase liquid chromatographic separations for analyte enrichment, intelligent selection of target fractions via on-line SRM monitoring of internal standards, and fraction multiplexing before nano–liquid chromatography-SRM quantification. Application of this strategy to human plasma/serum demonstrated accurate and reproducible quantification of proteins at concentrations in the 50–100 pg/mL range, which represents a major advance in the sensitivity of targeted protein quantification without the need for specific-affinity reagents. Application to a set of clinical serum samples illustrated an excellent correlation between the results obtained from the PRISM-SRM assay and those from clinical immunoassay for the prostate-specific antigen level.

A reversed-phase capillary ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) method for comprehensive top-down/bottom-up lipid profiling

Lipidomics is a critical part of metabolomics and aims to study all the lipids within a living system. We present here the development and evaluation of a sensitive capillary UPLC-MS method for comprehensive top-down/bottom-up lipid profiling. Three different stationary phases were evaluated in terms of peak capacity, linearity, reproducibility, and limit of quantification (LOQ) using a mixture of lipid standards representative of the lipidome. The relative standard deviations of the retention times and peak abundances of the lipid standards were 0.29% and 7.7%, respectively, when using the optimized method. The linearity was acceptable at >0.99 over 3 orders of magnitude, and the LOQs were sub-fmol. To demonstrate the performance of the method in the analysis of complex samples, we analyzed lipids extracted from a human cell line, rat plasma, and a model human skin tissue, identifying 446, 444, and 370 unique lipids, respectively. Overall, the method provided either higher coverage of the lipidome, greater measurement sensitivity, or both, when compared to other approaches of global, untargeted lipid profiling based on chromatography coupled with MS.

Targeted quantification of low ng/mL level proteins in human serum without immunoaffinity depletion

We recently reported an antibody-free targeted protein quantification strategy, termed high-pressure, high-resolution separations with intelligent selection and multiplexing (PRISM), for achieving significantly enhanced sensitivity using selected reaction monitoring (SRM) mass spectrometry. Integrating PRISM with front-end IgY14 immunoaffinity depletion, sensitive detection of targeted proteins at 50-100 pg/mL levels in human blood plasma/serum was demonstrated. However, immunoaffinity depletion is often associated with undesired losses of target proteins of interest. Herein we report further evaluation of PRISM-SRM quantification of low-abundance serum proteins without immunoaffinity depletion. Limits of quantification (LOQ) at low ng/mL levels with a median coefficient of variation (CV) of ∼12% were achieved for proteins spiked into human female serum. PRISM-SRM provided >100-fold improvement in the LOQ when compared to conventional LC-SRM measurements. PRISM-SRM was then applied to measure several low-abundance endogenous serum proteins, including prostate-specific antigen (PSA), in clinical prostate cancer patient sera. PRISM-SRM enabled confident detection of all target endogenous serum proteins except the low pg/mL-level cardiac troponin T. A correlation coefficient >0.99 was observed for PSA between the results from PRISM-SRM and immunoassays. Our results demonstrate that PRISM-SRM can successfully quantify low ng/mL proteins in human plasma or serum without depletion. We anticipate broad applications for PRISM-SRM quantification of low-abundance proteins in candidate biomarker verification and systems biology studies.

Serum proteomics reveals systemic dysregulation of innate immunity in type 1 diabetes.

Proteomics analysis identifies human serum proteins involved with innate immune responses, complement activation, and blood coagulation that are diagnostic for type 1 diabetes.

A Highly Sensitive Targeted Mass Spectrometric Assay for Quantification of AGR2 Protein in Human Urine and Serum

Anterior gradient 2 (AGR2) is a secreted, cancer-associated protein in many types of epithelial cancer cells. We developed a highly sensitive targeted mass spectrometric assay for quantification of AGR2 in urine and serum. Digested peptides from clinical samples were processed by PRISM (high pressure and high resolution separations coupled with intelligent selection and multiplexing), which incorporates high pH reversed-phase liquid chromatography (LC) separations to fractionate and select target fractions for follow-on LC-selected reaction monitoring (LC-SRM) analyses. The PRISM-SRM assay for AGR2 showed a reproducibility of <10% CV and limit of quantification (LOQ) values of ∼130 pg/mL in serum and ∼10 pg per 100 μg of total protein mass in urine, respectively. A good correlation (R(2) = 0.91) was observed for the measurable AGR2 concentrations in urine between SRM and enzyme-linked immunosorbent assay (ELISA). On the basis of an initial cohort of 37 subjects, urinary AGR2/PSA concentration ratios showed a significant difference (P = 0.026) between noncancer and cancer. Large clinical cohort studies are needed for the validation of AGR2 as a useful diagnostic biomarker for prostate cancer. Our work validated the approach of identifying candidate secreted protein biomarkers through genomics and measurement by targeted proteomics, especially for proteins where no immunoassays are available.

Analysis of serum total and free PSA using immunoaffinity depletion coupled to SRM: correlation with clinical immunoassay tests.

Recently, selected reaction monitoring mass spectrometry (SRM-MS) has been more frequently applied to measure low abundance biomarker candidates in tissues and biofluids, owing to its high sensitivity and specificity, simplicity of assay configuration, and exceptional multiplexing capability. In this study, we report for the first time the development of immunoaffinity depletion-based workflows and SRM-MS assays that enable sensitive and accurate quantification of total and free prostate-specific antigen (PSA) in serum without the requirement for specific PSA antibodies. Low ng/mL level detection of both total and free PSA was consistently achieved in both PSA-spiked female serum samples and actual patient serum samples. Moreover, comparison of the results obtained when SRM PSA assays and conventional immunoassays were applied to the same samples showed good correlation in several independent clinical serum sample sets. These results demonstrate that the workflows and SRM assays developed here provide an attractive alternative for reliably measuring candidate biomarkers in human blood, without the need to develop affinity reagents. Furthermore, the simultaneous measurement of multiple biomarkers, including the free and bound forms of PSA, can be performed in a single multiplexed analysis using high-resolution liquid chromatographic separation coupled with SRM-MS. This article is part of a Special Issue entitled: Translational Proteomics.

Long-gradient separations coupled with selected reaction monitoring for highly sensitive, large scale targeted protein quantification in a single analysis.

Long-gradient separations coupled to tandem mass spectrometry (MS) were recently demonstrated to provide a deep proteome coverage for global proteomics; however, such long-gradient separations have not been explored for targeted proteomics. Herein, we investigate the potential performance of the long-gradient separations coupled with selected reaction monitoring (LG-SRM) for targeted protein quantification. Direct comparison of LG-SRM (5 h gradient) and conventional liquid chromatography (LC)-SRM (45 min gradient) showed that the long-gradient separations significantly reduced background interference levels and provided an 8- to 100-fold improvement in limit of quantification (LOQ) for target proteins in human female serum. On the basis of at least one surrogate peptide per protein, an LOQ of 10 ng/mL was achieved for the two spiked proteins in nondepleted human serum. The LG-SRM detection of seven out of eight endogenous plasma proteins expressed at ng/mL or subng/mL levels in clinical patient sera was also demonstrated. A correlation coefficient of >0.99 was observed for the results of LG-SRM and enzyme-linked immunosorbent assay (ELISA) measurements for prostate-specific antigen (PSA) in selected patient sera. Further enhancement of LG-SRM sensitivity was achieved by applying front-end IgY14 immunoaffinity depletion. Besides improved sensitivity, LG-SRM potentially offers much higher multiplexing capacity than conventional LC-SRM due to an increase in average peak widths (~3-fold) for a 300 min gradient compared to a 45 min gradient. Therefore, LG-SRM holds great potential for bridging the gap between global and targeted proteomics due to its advantages in both sensitivity and multiplexing capacity.

Mining the human urine proteome for monitoring renal transplant injury

The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.

Comprehensive Quantitative Analysis of Ovarian and Breast Cancer Tumor Peptidomes

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches.

Expediting SRM assay development for large-scale targeted proteomics experiments.

Because of its high sensitivity and specificity, selected reaction monitoring (SRM)-based targeted proteomics has become increasingly popular for biological and translational applications. Selection of optimal transitions and optimization of collision energy (CE) are important assay development steps for achieving sensitive detection and accurate quantification; however, these steps can be labor-intensive, especially for large-scale applications. Herein, we explored several options for accelerating SRM assay development evaluated in the context of a relatively large set of 215 synthetic peptide targets. We first showed that HCD fragmentation is very similar to that of CID in triple quadrupole (QQQ) instrumentation and that by selection of the top 6 y fragment ions from HCD spectra, >86% of the top transitions optimized from direct infusion with QQQ instrumentation are covered. We also demonstrated that the CE calculated by existing prediction tools was less accurate for 3+ precursors and that a significant increase in intensity for transitions could be obtained using a new CE prediction equation constructed from the present experimental data. Overall, our study illustrated the feasibility of expediting the development of larger numbers of high-sensitivity SRM assays through automation of transition selection and accurate prediction of optimal CE to improve both SRM throughput and measurement quality.